Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations9994
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.4 MiB
Average record size in memory981.9 B

Variable types

Numeric6
Text6
DateTime2
Categorical9

Alerts

Country has constant value "United States" Constant
Category is highly overall correlated with Sub-CategoryHigh correlation
Discount is highly overall correlated with ProfitHigh correlation
Postal Code is highly overall correlated with Region and 2 other fieldsHigh correlation
Profit is highly overall correlated with Discount and 1 other fieldsHigh correlation
Region is highly overall correlated with Postal Code and 2 other fieldsHigh correlation
Retail Sales People is highly overall correlated with Postal Code and 2 other fieldsHigh correlation
Sales is highly overall correlated with ProfitHigh correlation
State is highly overall correlated with Postal Code and 2 other fieldsHigh correlation
Sub-Category is highly overall correlated with CategoryHigh correlation
Returned is highly imbalanced (59.8%) Imbalance
Row ID is uniformly distributed Uniform
Row ID has unique values Unique
Discount has 4798 (48.0%) zeros Zeros

Reproduction

Analysis started2025-02-21 05:42:16.960811
Analysis finished2025-02-21 05:42:20.351097
Duration3.39 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Row ID
Real number (ℝ)

Uniform  Unique 

Distinct9994
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4997.5
Minimum1
Maximum9994
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-02-20T21:42:20.392074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile500.65
Q12499.25
median4997.5
Q37495.75
95-th percentile9494.35
Maximum9994
Range9993
Interquartile range (IQR)4996.5

Descriptive statistics

Standard deviation2885.1636
Coefficient of variation (CV)0.57732139
Kurtosis-1.2
Mean4997.5
Median Absolute Deviation (MAD)2498.5
Skewness0
Sum49945015
Variance8324169.2
MonotonicityStrictly increasing
2025-02-20T21:42:20.480182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9994 1
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
Other values (9984) 9984
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
9994 1
< 0.1%
9993 1
< 0.1%
9992 1
< 0.1%
9991 1
< 0.1%
9990 1
< 0.1%
9989 1
< 0.1%
9988 1
< 0.1%
9987 1
< 0.1%
9986 1
< 0.1%
9985 1
< 0.1%
Distinct5009
Distinct (%)50.1%
Missing0
Missing (%)0.0%
Memory size615.0 KiB
2025-02-20T21:42:20.586588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters139916
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2538 ?
Unique (%)25.4%

Sample

1st rowCA-2016-152156
2nd rowCA-2016-152156
3rd rowCA-2016-138688
4th rowUS-2015-108966
5th rowUS-2015-108966
ValueCountFrequency (%)
ca-2017-100111 14
 
0.1%
ca-2017-157987 12
 
0.1%
us-2016-108504 11
 
0.1%
ca-2016-165330 11
 
0.1%
ca-2015-131338 10
 
0.1%
us-2015-126977 10
 
0.1%
ca-2016-105732 10
 
0.1%
ca-2016-145177 9
 
0.1%
ca-2017-117457 9
 
0.1%
ca-2017-140949 9
 
0.1%
Other values (4999) 9889
98.9%
2025-02-20T21:42:20.726012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 25510
18.2%
- 19988
14.3%
0 15492
11.1%
2 15381
11.0%
C 8308
 
5.9%
A 8308
 
5.9%
6 7904
 
5.6%
7 7438
 
5.3%
4 7400
 
5.3%
5 7338
 
5.2%
Other values (5) 16849
12.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 99940
71.4%
Dash Punctuation 19988
 
14.3%
Uppercase Letter 19988
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 25510
25.5%
0 15492
15.5%
2 15381
15.4%
6 7904
 
7.9%
7 7438
 
7.4%
4 7400
 
7.4%
5 7338
 
7.3%
3 5449
 
5.5%
8 4042
 
4.0%
9 3986
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
C 8308
41.6%
A 8308
41.6%
U 1686
 
8.4%
S 1686
 
8.4%
Dash Punctuation
ValueCountFrequency (%)
- 19988
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 119928
85.7%
Latin 19988
 
14.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 25510
21.3%
- 19988
16.7%
0 15492
12.9%
2 15381
12.8%
6 7904
 
6.6%
7 7438
 
6.2%
4 7400
 
6.2%
5 7338
 
6.1%
3 5449
 
4.5%
8 4042
 
3.4%
Latin
ValueCountFrequency (%)
C 8308
41.6%
A 8308
41.6%
U 1686
 
8.4%
S 1686
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 139916
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 25510
18.2%
- 19988
14.3%
0 15492
11.1%
2 15381
11.0%
C 8308
 
5.9%
A 8308
 
5.9%
6 7904
 
5.6%
7 7438
 
5.3%
4 7400
 
5.3%
5 7338
 
5.2%
Other values (5) 16849
12.0%
Distinct1237
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
Minimum2014-01-02 00:00:00
Maximum2017-12-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-20T21:42:20.824383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:20.918299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1215
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
Minimum2014-01-15 00:00:00
Maximum2018-05-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-20T21:42:20.986929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:21.049128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Ship Mode
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size603.5 KiB
Standard Class
5968 
Second Class
1945 
First Class
1538 
Same Day
 
543

Length

Max length14
Median length14
Mean length12.823094
Min length8

Characters and Unicode

Total characters128154
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSecond Class
2nd rowSecond Class
3rd rowSecond Class
4th rowStandard Class
5th rowStandard Class

Common Values

ValueCountFrequency (%)
Standard Class 5968
59.7%
Second Class 1945
 
19.5%
First Class 1538
 
15.4%
Same Day 543
 
5.4%

Length

2025-02-20T21:42:21.104522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T21:42:21.138721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
class 9451
47.3%
standard 5968
29.9%
second 1945
 
9.7%
first 1538
 
7.7%
same 543
 
2.7%
day 543
 
2.7%

Most occurring characters

ValueCountFrequency (%)
a 22473
17.5%
s 20440
15.9%
d 13881
10.8%
9994
7.8%
C 9451
7.4%
l 9451
7.4%
S 8456
 
6.6%
n 7913
 
6.2%
t 7506
 
5.9%
r 7506
 
5.9%
Other values (8) 11083
8.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 98172
76.6%
Uppercase Letter 19988
 
15.6%
Space Separator 9994
 
7.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 22473
22.9%
s 20440
20.8%
d 13881
14.1%
l 9451
9.6%
n 7913
 
8.1%
t 7506
 
7.6%
r 7506
 
7.6%
e 2488
 
2.5%
c 1945
 
2.0%
o 1945
 
2.0%
Other values (3) 2624
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
C 9451
47.3%
S 8456
42.3%
F 1538
 
7.7%
D 543
 
2.7%
Space Separator
ValueCountFrequency (%)
9994
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 118160
92.2%
Common 9994
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 22473
19.0%
s 20440
17.3%
d 13881
11.7%
C 9451
8.0%
l 9451
8.0%
S 8456
 
7.2%
n 7913
 
6.7%
t 7506
 
6.4%
r 7506
 
6.4%
e 2488
 
2.1%
Other values (7) 8595
 
7.3%
Common
ValueCountFrequency (%)
9994
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 128154
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 22473
17.5%
s 20440
15.9%
d 13881
10.8%
9994
7.8%
C 9451
7.4%
l 9451
7.4%
S 8456
 
6.6%
n 7913
 
6.2%
t 7506
 
5.9%
r 7506
 
5.9%
Other values (8) 11083
8.6%
Distinct793
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size556.4 KiB
2025-02-20T21:42:21.260078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters79952
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowCG-12520
2nd rowCG-12520
3rd rowDV-13045
4th rowSO-20335
5th rowSO-20335
ValueCountFrequency (%)
wb-21850 37
 
0.4%
pp-18955 34
 
0.3%
jl-15835 34
 
0.3%
ma-17560 34
 
0.3%
eh-13765 32
 
0.3%
ck-12205 32
 
0.3%
jd-15895 32
 
0.3%
sv-20365 32
 
0.3%
ap-10915 31
 
0.3%
ep-13915 31
 
0.3%
Other values (783) 9665
96.7%
2025-02-20T21:42:21.418203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 11915
14.9%
- 9994
12.5%
0 8532
 
10.7%
5 7865
 
9.8%
2 4682
 
5.9%
7 2931
 
3.7%
6 2909
 
3.6%
9 2904
 
3.6%
8 2818
 
3.5%
3 2779
 
3.5%
Other values (30) 22623
28.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49970
62.5%
Uppercase Letter 19945
 
24.9%
Dash Punctuation 9994
 
12.5%
Lowercase Letter 43
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 1798
 
9.0%
C 1725
 
8.6%
M 1712
 
8.6%
B 1642
 
8.2%
D 1296
 
6.5%
A 1227
 
6.2%
J 1134
 
5.7%
P 1105
 
5.5%
H 968
 
4.9%
K 932
 
4.7%
Other values (16) 6406
32.1%
Decimal Number
ValueCountFrequency (%)
1 11915
23.8%
0 8532
17.1%
5 7865
15.7%
2 4682
 
9.4%
7 2931
 
5.9%
6 2909
 
5.8%
9 2904
 
5.8%
8 2818
 
5.6%
3 2779
 
5.6%
4 2635
 
5.3%
Lowercase Letter
ValueCountFrequency (%)
p 29
67.4%
o 8
 
18.6%
l 6
 
14.0%
Dash Punctuation
ValueCountFrequency (%)
- 9994
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 59964
75.0%
Latin 19988
 
25.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 1798
 
9.0%
C 1725
 
8.6%
M 1712
 
8.6%
B 1642
 
8.2%
D 1296
 
6.5%
A 1227
 
6.1%
J 1134
 
5.7%
P 1105
 
5.5%
H 968
 
4.8%
K 932
 
4.7%
Other values (19) 6449
32.3%
Common
ValueCountFrequency (%)
1 11915
19.9%
- 9994
16.7%
0 8532
14.2%
5 7865
13.1%
2 4682
 
7.8%
7 2931
 
4.9%
6 2909
 
4.9%
9 2904
 
4.8%
8 2818
 
4.7%
3 2779
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 11915
14.9%
- 9994
12.5%
0 8532
 
10.7%
5 7865
 
9.8%
2 4682
 
5.9%
7 2931
 
3.7%
6 2909
 
3.6%
9 2904
 
3.6%
8 2818
 
3.5%
3 2779
 
3.5%
Other values (30) 22623
28.3%
Distinct793
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size607.6 KiB
2025-02-20T21:42:21.562778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length22
Median length18
Mean length12.960676
Min length7

Characters and Unicode

Total characters129529
Distinct characters57
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowClaire Gute
2nd rowClaire Gute
3rd rowDarrin Van Huff
4th rowSean O'Donnell
5th rowSean O'Donnell
ValueCountFrequency (%)
michael 120
 
0.6%
frank 112
 
0.6%
john 107
 
0.5%
patrick 96
 
0.5%
stewart 93
 
0.5%
brian 93
 
0.5%
paul 92
 
0.5%
rick 91
 
0.5%
ken 91
 
0.5%
matt 86
 
0.4%
Other values (901) 19072
95.1%
2025-02-20T21:42:21.754866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 12011
 
9.3%
e 11836
 
9.1%
n 10241
 
7.9%
10059
 
7.8%
r 9530
 
7.4%
i 7919
 
6.1%
l 6494
 
5.0%
o 5850
 
4.5%
t 5435
 
4.2%
s 4546
 
3.5%
Other values (47) 45608
35.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 98856
76.3%
Uppercase Letter 20461
 
15.8%
Space Separator 10059
 
7.8%
Other Punctuation 124
 
0.1%
Dash Punctuation 29
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 12011
12.1%
e 11836
12.0%
n 10241
10.4%
r 9530
9.6%
i 7919
 
8.0%
l 6494
 
6.6%
o 5850
 
5.9%
t 5435
 
5.5%
s 4546
 
4.6%
h 3857
 
3.9%
Other values (18) 21137
21.4%
Uppercase Letter
ValueCountFrequency (%)
C 1830
 
8.9%
S 1798
 
8.8%
M 1749
 
8.5%
B 1696
 
8.3%
D 1325
 
6.5%
A 1282
 
6.3%
J 1134
 
5.5%
P 1105
 
5.4%
H 1005
 
4.9%
K 964
 
4.7%
Other values (16) 6573
32.1%
Space Separator
ValueCountFrequency (%)
10059
100.0%
Other Punctuation
ValueCountFrequency (%)
' 124
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 119317
92.1%
Common 10212
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 12011
 
10.1%
e 11836
 
9.9%
n 10241
 
8.6%
r 9530
 
8.0%
i 7919
 
6.6%
l 6494
 
5.4%
o 5850
 
4.9%
t 5435
 
4.6%
s 4546
 
3.8%
h 3857
 
3.2%
Other values (44) 41598
34.9%
Common
ValueCountFrequency (%)
10059
98.5%
' 124
 
1.2%
- 29
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129440
99.9%
None 89
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 12011
 
9.3%
e 11836
 
9.1%
n 10241
 
7.9%
10059
 
7.8%
r 9530
 
7.4%
i 7919
 
6.1%
l 6494
 
5.0%
o 5850
 
4.5%
t 5435
 
4.2%
s 4546
 
3.5%
Other values (44) 45519
35.2%
None
ValueCountFrequency (%)
ö 61
68.5%
ä 23
 
25.8%
ü 5
 
5.6%

Segment
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size564.6 KiB
Consumer
5191 
Corporate
3020 
Home Office
1783 

Length

Max length11
Median length8
Mean length8.8374024
Min length8

Characters and Unicode

Total characters88321
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConsumer
2nd rowConsumer
3rd rowCorporate
4th rowConsumer
5th rowConsumer

Common Values

ValueCountFrequency (%)
Consumer 5191
51.9%
Corporate 3020
30.2%
Home Office 1783
 
17.8%

Length

2025-02-20T21:42:21.803867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T21:42:21.835308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
consumer 5191
44.1%
corporate 3020
25.6%
home 1783
 
15.1%
office 1783
 
15.1%

Most occurring characters

ValueCountFrequency (%)
o 13014
14.7%
e 11777
13.3%
r 11231
12.7%
C 8211
9.3%
m 6974
7.9%
u 5191
 
5.9%
s 5191
 
5.9%
n 5191
 
5.9%
f 3566
 
4.0%
p 3020
 
3.4%
Other values (7) 14955
16.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 74761
84.6%
Uppercase Letter 11777
 
13.3%
Space Separator 1783
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 13014
17.4%
e 11777
15.8%
r 11231
15.0%
m 6974
9.3%
u 5191
 
6.9%
s 5191
 
6.9%
n 5191
 
6.9%
f 3566
 
4.8%
p 3020
 
4.0%
a 3020
 
4.0%
Other values (3) 6586
8.8%
Uppercase Letter
ValueCountFrequency (%)
C 8211
69.7%
H 1783
 
15.1%
O 1783
 
15.1%
Space Separator
ValueCountFrequency (%)
1783
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 86538
98.0%
Common 1783
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 13014
15.0%
e 11777
13.6%
r 11231
13.0%
C 8211
9.5%
m 6974
8.1%
u 5191
 
6.0%
s 5191
 
6.0%
n 5191
 
6.0%
f 3566
 
4.1%
p 3020
 
3.5%
Other values (6) 13172
15.2%
Common
ValueCountFrequency (%)
1783
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88321
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 13014
14.7%
e 11777
13.3%
r 11231
12.7%
C 8211
9.3%
m 6974
7.9%
u 5191
 
5.9%
s 5191
 
5.9%
n 5191
 
5.9%
f 3566
 
4.0%
p 3020
 
3.4%
Other values (7) 14955
16.9%

Country
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size605.2 KiB
United States
9994 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters129922
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnited States
2nd rowUnited States
3rd rowUnited States
4th rowUnited States
5th rowUnited States

Common Values

ValueCountFrequency (%)
United States 9994
100.0%

Length

2025-02-20T21:42:21.876173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T21:42:21.900055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
united 9994
50.0%
states 9994
50.0%

Most occurring characters

ValueCountFrequency (%)
t 29982
23.1%
e 19988
15.4%
n 9994
 
7.7%
U 9994
 
7.7%
i 9994
 
7.7%
d 9994
 
7.7%
9994
 
7.7%
S 9994
 
7.7%
a 9994
 
7.7%
s 9994
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 99940
76.9%
Uppercase Letter 19988
 
15.4%
Space Separator 9994
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 29982
30.0%
e 19988
20.0%
n 9994
 
10.0%
i 9994
 
10.0%
d 9994
 
10.0%
a 9994
 
10.0%
s 9994
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
U 9994
50.0%
S 9994
50.0%
Space Separator
ValueCountFrequency (%)
9994
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 119928
92.3%
Common 9994
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 29982
25.0%
e 19988
16.7%
n 9994
 
8.3%
U 9994
 
8.3%
i 9994
 
8.3%
d 9994
 
8.3%
S 9994
 
8.3%
a 9994
 
8.3%
s 9994
 
8.3%
Common
ValueCountFrequency (%)
9994
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 29982
23.1%
e 19988
15.4%
n 9994
 
7.7%
U 9994
 
7.7%
i 9994
 
7.7%
d 9994
 
7.7%
9994
 
7.7%
S 9994
 
7.7%
a 9994
 
7.7%
s 9994
 
7.7%

City
Text

Distinct531
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size569.4 KiB
2025-02-20T21:42:22.033124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length14
Mean length9.3306984
Min length4

Characters and Unicode

Total characters93251
Distinct characters51
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)0.7%

Sample

1st rowHenderson
2nd rowHenderson
3rd rowLos Angeles
4th rowFort Lauderdale
5th rowFort Lauderdale
ValueCountFrequency (%)
city 994
 
7.0%
new 937
 
6.6%
york 920
 
6.5%
san 805
 
5.7%
angeles 747
 
5.2%
los 747
 
5.2%
philadelphia 537
 
3.8%
francisco 510
 
3.6%
seattle 428
 
3.0%
houston 377
 
2.6%
Other values (555) 7234
50.8%
2025-02-20T21:42:22.224303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 8719
 
9.4%
a 7591
 
8.1%
o 7499
 
8.0%
i 6229
 
6.7%
n 6199
 
6.6%
l 5986
 
6.4%
s 4699
 
5.0%
r 4468
 
4.8%
t 4438
 
4.8%
4242
 
4.5%
Other values (41) 33181
35.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 74773
80.2%
Uppercase Letter 14236
 
15.3%
Space Separator 4242
 
4.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8719
11.7%
a 7591
10.2%
o 7499
10.0%
i 6229
 
8.3%
n 6199
 
8.3%
l 5986
 
8.0%
s 4699
 
6.3%
r 4468
 
6.0%
t 4438
 
5.9%
c 2393
 
3.2%
Other values (16) 16552
22.1%
Uppercase Letter
ValueCountFrequency (%)
C 2085
14.6%
S 1740
12.2%
L 1295
9.1%
A 1242
8.7%
N 1134
8.0%
P 1013
 
7.1%
Y 940
 
6.6%
F 794
 
5.6%
D 627
 
4.4%
H 617
 
4.3%
Other values (14) 2749
19.3%
Space Separator
ValueCountFrequency (%)
4242
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 89009
95.5%
Common 4242
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8719
 
9.8%
a 7591
 
8.5%
o 7499
 
8.4%
i 6229
 
7.0%
n 6199
 
7.0%
l 5986
 
6.7%
s 4699
 
5.3%
r 4468
 
5.0%
t 4438
 
5.0%
c 2393
 
2.7%
Other values (40) 30788
34.6%
Common
ValueCountFrequency (%)
4242
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8719
 
9.4%
a 7591
 
8.1%
o 7499
 
8.0%
i 6229
 
6.7%
n 6199
 
6.6%
l 5986
 
6.4%
s 4699
 
5.0%
r 4468
 
4.8%
t 4438
 
4.8%
4242
 
4.5%
Other values (41) 33181
35.6%

State
Categorical

High correlation 

Distinct49
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size561.2 KiB
California
2001 
New York
1128 
Texas
985 
Pennsylvania
587 
Washington
506 
Other values (44)
4787 

Length

Max length20
Median length14
Mean length8.4871923
Min length4

Characters and Unicode

Total characters84821
Distinct characters46
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowKentucky
2nd rowKentucky
3rd rowCalifornia
4th rowFlorida
5th rowFlorida

Common Values

ValueCountFrequency (%)
California 2001
20.0%
New York 1128
 
11.3%
Texas 985
 
9.9%
Pennsylvania 587
 
5.9%
Washington 506
 
5.1%
Illinois 492
 
4.9%
Ohio 469
 
4.7%
Florida 383
 
3.8%
Michigan 255
 
2.6%
North Carolina 249
 
2.5%
Other values (39) 2939
29.4%

Length

2025-02-20T21:42:22.276768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
california 2001
17.1%
new 1322
 
11.3%
york 1128
 
9.6%
texas 985
 
8.4%
pennsylvania 587
 
5.0%
washington 506
 
4.3%
illinois 492
 
4.2%
ohio 469
 
4.0%
florida 383
 
3.3%
carolina 291
 
2.5%
Other values (43) 3542
30.3%

Most occurring characters

ValueCountFrequency (%)
a 10758
12.7%
i 9895
11.7%
n 8090
 
9.5%
o 7323
 
8.6%
r 5544
 
6.5%
e 5051
 
6.0%
l 4822
 
5.7%
s 4604
 
5.4%
C 2566
 
3.0%
f 2011
 
2.4%
Other values (36) 24157
28.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 71413
84.2%
Uppercase Letter 11696
 
13.8%
Space Separator 1712
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 10758
15.1%
i 9895
13.9%
n 8090
11.3%
o 7323
10.3%
r 5544
7.8%
e 5051
7.1%
l 4822
6.8%
s 4604
6.4%
f 2011
 
2.8%
h 1898
 
2.7%
Other values (14) 11417
16.0%
Uppercase Letter
ValueCountFrequency (%)
C 2566
21.9%
N 1655
14.2%
T 1168
10.0%
Y 1128
9.6%
M 763
 
6.5%
I 748
 
6.4%
O 659
 
5.6%
W 621
 
5.3%
P 587
 
5.0%
F 383
 
3.3%
Other values (11) 1418
12.1%
Space Separator
ValueCountFrequency (%)
1712
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 83109
98.0%
Common 1712
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 10758
12.9%
i 9895
11.9%
n 8090
 
9.7%
o 7323
 
8.8%
r 5544
 
6.7%
e 5051
 
6.1%
l 4822
 
5.8%
s 4604
 
5.5%
C 2566
 
3.1%
f 2011
 
2.4%
Other values (35) 22445
27.0%
Common
ValueCountFrequency (%)
1712
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84821
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 10758
12.7%
i 9895
11.7%
n 8090
 
9.5%
o 7323
 
8.6%
r 5544
 
6.5%
e 5051
 
6.0%
l 4822
 
5.7%
s 4604
 
5.4%
C 2566
 
3.0%
f 2011
 
2.4%
Other values (36) 24157
28.5%

Postal Code
Real number (ℝ)

High correlation 

Distinct631
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55190.379
Minimum1040
Maximum99301
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-02-20T21:42:22.330281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1040
5-th percentile10009
Q123223
median56430.5
Q390008
95-th percentile98006
Maximum99301
Range98261
Interquartile range (IQR)66785

Descriptive statistics

Standard deviation32063.693
Coefficient of variation (CV)0.58096526
Kurtosis-1.4930202
Mean55190.379
Median Absolute Deviation (MAD)33573.5
Skewness-0.12852552
Sum5.5157265 × 108
Variance1.0280804 × 109
MonotonicityNot monotonic
2025-02-20T21:42:22.383016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10035 263
 
2.6%
10024 230
 
2.3%
10009 229
 
2.3%
94122 203
 
2.0%
10011 193
 
1.9%
94110 166
 
1.7%
98105 165
 
1.7%
19134 160
 
1.6%
90049 151
 
1.5%
98103 151
 
1.5%
Other values (621) 8083
80.9%
ValueCountFrequency (%)
1040 1
 
< 0.1%
1453 6
 
0.1%
1752 2
 
< 0.1%
1810 4
 
< 0.1%
1841 33
0.3%
1852 16
0.2%
1915 3
 
< 0.1%
2038 17
0.2%
2138 6
 
0.1%
2148 3
 
< 0.1%
ValueCountFrequency (%)
99301 6
 
0.1%
99207 7
 
0.1%
98661 5
 
0.1%
98632 3
 
< 0.1%
98502 5
 
0.1%
98270 2
 
< 0.1%
98226 3
 
< 0.1%
98208 1
 
< 0.1%
98198 7
 
0.1%
98115 112
1.1%

Region
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size525.8 KiB
West
3203 
East
2848 
Central
2323 
South
1620 

Length

Max length7
Median length4
Mean length4.8594156
Min length4

Characters and Unicode

Total characters48565
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth
2nd rowSouth
3rd rowWest
4th rowSouth
5th rowSouth

Common Values

ValueCountFrequency (%)
West 3203
32.0%
East 2848
28.5%
Central 2323
23.2%
South 1620
16.2%

Length

2025-02-20T21:42:22.434298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T21:42:22.469800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
west 3203
32.0%
east 2848
28.5%
central 2323
23.2%
south 1620
16.2%

Most occurring characters

ValueCountFrequency (%)
t 9994
20.6%
s 6051
12.5%
e 5526
11.4%
a 5171
10.6%
W 3203
 
6.6%
E 2848
 
5.9%
C 2323
 
4.8%
n 2323
 
4.8%
r 2323
 
4.8%
l 2323
 
4.8%
Other values (4) 6480
13.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 38571
79.4%
Uppercase Letter 9994
 
20.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 9994
25.9%
s 6051
15.7%
e 5526
14.3%
a 5171
13.4%
n 2323
 
6.0%
r 2323
 
6.0%
l 2323
 
6.0%
o 1620
 
4.2%
u 1620
 
4.2%
h 1620
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
W 3203
32.0%
E 2848
28.5%
C 2323
23.2%
S 1620
16.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 48565
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 9994
20.6%
s 6051
12.5%
e 5526
11.4%
a 5171
10.6%
W 3203
 
6.6%
E 2848
 
5.9%
C 2323
 
4.8%
n 2323
 
4.8%
r 2323
 
4.8%
l 2323
 
4.8%
Other values (4) 6480
13.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48565
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 9994
20.6%
s 6051
12.5%
e 5526
11.4%
a 5171
10.6%
W 3203
 
6.6%
E 2848
 
5.9%
C 2323
 
4.8%
n 2323
 
4.8%
r 2323
 
4.8%
l 2323
 
4.8%
Other values (4) 6480
13.3%

Retail Sales People
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size608.3 KiB
Anna Andreadi
3203 
Chuck Magee
2848 
Kelly Williams
2323 
Cassandra Brandow
1620 

Length

Max length17
Median length14
Mean length13.310887
Min length11

Characters and Unicode

Total characters133029
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCassandra Brandow
2nd rowCassandra Brandow
3rd rowAnna Andreadi
4th rowCassandra Brandow
5th rowCassandra Brandow

Common Values

ValueCountFrequency (%)
Anna Andreadi 3203
32.0%
Chuck Magee 2848
28.5%
Kelly Williams 2323
23.2%
Cassandra Brandow 1620
16.2%

Length

2025-02-20T21:42:22.515505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T21:42:22.547844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
anna 3203
16.0%
andreadi 3203
16.0%
chuck 2848
14.2%
magee 2848
14.2%
kelly 2323
11.6%
williams 2323
11.6%
cassandra 1620
8.1%
brandow 1620
8.1%

Most occurring characters

ValueCountFrequency (%)
a 18057
13.6%
n 12849
 
9.7%
e 11222
 
8.4%
9994
 
7.5%
d 9646
 
7.3%
l 9292
 
7.0%
i 7849
 
5.9%
r 6443
 
4.8%
A 6406
 
4.8%
s 5563
 
4.2%
Other values (14) 35708
26.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 103047
77.5%
Uppercase Letter 19988
 
15.0%
Space Separator 9994
 
7.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 18057
17.5%
n 12849
12.5%
e 11222
10.9%
d 9646
9.4%
l 9292
9.0%
i 7849
7.6%
r 6443
 
6.3%
s 5563
 
5.4%
h 2848
 
2.8%
k 2848
 
2.8%
Other values (7) 16430
15.9%
Uppercase Letter
ValueCountFrequency (%)
A 6406
32.0%
C 4468
22.4%
M 2848
14.2%
K 2323
 
11.6%
W 2323
 
11.6%
B 1620
 
8.1%
Space Separator
ValueCountFrequency (%)
9994
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 123035
92.5%
Common 9994
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 18057
14.7%
n 12849
10.4%
e 11222
 
9.1%
d 9646
 
7.8%
l 9292
 
7.6%
i 7849
 
6.4%
r 6443
 
5.2%
A 6406
 
5.2%
s 5563
 
4.5%
C 4468
 
3.6%
Other values (13) 31240
25.4%
Common
ValueCountFrequency (%)
9994
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 133029
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 18057
13.6%
n 12849
 
9.7%
e 11222
 
8.4%
9994
 
7.5%
d 9646
 
7.3%
l 9292
 
7.0%
i 7849
 
5.9%
r 6443
 
4.8%
A 6406
 
4.8%
s 5563
 
4.2%
Other values (14) 35708
26.8%
Distinct1862
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Memory size624.8 KiB
2025-02-20T21:42:22.645345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters149910
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)0.9%

Sample

1st rowFUR-BO-10001798
2nd rowFUR-CH-10000454
3rd rowOFF-LA-10000240
4th rowFUR-TA-10000577
5th rowOFF-ST-10000760
ValueCountFrequency (%)
off-pa-10001970 19
 
0.2%
tec-ac-10003832 18
 
0.2%
fur-fu-10004270 16
 
0.2%
tec-ac-10003628 15
 
0.2%
tec-ac-10002049 15
 
0.2%
fur-ch-10001146 15
 
0.2%
fur-ch-10002647 15
 
0.2%
off-bi-10001524 14
 
0.1%
fur-ch-10003774 14
 
0.1%
off-pa-10002377 14
 
0.1%
Other values (1852) 9839
98.4%
2025-02-20T21:42:22.778724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 35052
23.4%
- 19988
13.3%
F 15347
10.2%
1 14995
10.0%
O 6322
 
4.2%
2 4862
 
3.2%
4 4831
 
3.2%
3 4805
 
3.2%
A 4422
 
2.9%
5 3401
 
2.3%
Other values (17) 35885
23.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79952
53.3%
Uppercase Letter 49970
33.3%
Dash Punctuation 19988
 
13.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 15347
30.7%
O 6322
12.7%
A 4422
 
8.8%
C 3307
 
6.6%
U 3268
 
6.5%
T 3012
 
6.0%
R 2917
 
5.8%
P 2725
 
5.5%
E 2101
 
4.2%
B 1751
 
3.5%
Other values (6) 4798
 
9.6%
Decimal Number
ValueCountFrequency (%)
0 35052
43.8%
1 14995
18.8%
2 4862
 
6.1%
4 4831
 
6.0%
3 4805
 
6.0%
5 3401
 
4.3%
7 3103
 
3.9%
9 3051
 
3.8%
6 2999
 
3.8%
8 2853
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 19988
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 99940
66.7%
Latin 49970
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 15347
30.7%
O 6322
12.7%
A 4422
 
8.8%
C 3307
 
6.6%
U 3268
 
6.5%
T 3012
 
6.0%
R 2917
 
5.8%
P 2725
 
5.5%
E 2101
 
4.2%
B 1751
 
3.5%
Other values (6) 4798
 
9.6%
Common
ValueCountFrequency (%)
0 35052
35.1%
- 19988
20.0%
1 14995
15.0%
2 4862
 
4.9%
4 4831
 
4.8%
3 4805
 
4.8%
5 3401
 
3.4%
7 3103
 
3.1%
9 3051
 
3.1%
6 2999
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 149910
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 35052
23.4%
- 19988
13.3%
F 15347
10.2%
1 14995
10.0%
O 6322
 
4.2%
2 4862
 
3.2%
4 4831
 
3.2%
3 4805
 
3.2%
A 4422
 
2.9%
5 3401
 
2.3%
Other values (17) 35885
23.9%

Category
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size603.3 KiB
Office Supplies
6026 
Furniture
2121 
Technology
1847 

Length

Max length15
Median length15
Mean length12.802582
Min length9

Characters and Unicode

Total characters127949
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFurniture
2nd rowFurniture
3rd rowOffice Supplies
4th rowFurniture
5th rowOffice Supplies

Common Values

ValueCountFrequency (%)
Office Supplies 6026
60.3%
Furniture 2121
 
21.2%
Technology 1847
 
18.5%

Length

2025-02-20T21:42:22.826956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T21:42:22.858471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
office 6026
37.6%
supplies 6026
37.6%
furniture 2121
 
13.2%
technology 1847
 
11.5%

Most occurring characters

ValueCountFrequency (%)
e 16020
12.5%
i 14173
11.1%
p 12052
9.4%
f 12052
9.4%
u 10268
 
8.0%
c 7873
 
6.2%
l 7873
 
6.2%
O 6026
 
4.7%
S 6026
 
4.7%
6026
 
4.7%
Other values (10) 29560
23.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 105903
82.8%
Uppercase Letter 16020
 
12.5%
Space Separator 6026
 
4.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 16020
15.1%
i 14173
13.4%
p 12052
11.4%
f 12052
11.4%
u 10268
9.7%
c 7873
7.4%
l 7873
7.4%
s 6026
 
5.7%
r 4242
 
4.0%
n 3968
 
3.7%
Other values (5) 11356
10.7%
Uppercase Letter
ValueCountFrequency (%)
O 6026
37.6%
S 6026
37.6%
F 2121
 
13.2%
T 1847
 
11.5%
Space Separator
ValueCountFrequency (%)
6026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 121923
95.3%
Common 6026
 
4.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 16020
13.1%
i 14173
11.6%
p 12052
9.9%
f 12052
9.9%
u 10268
8.4%
c 7873
 
6.5%
l 7873
 
6.5%
O 6026
 
4.9%
S 6026
 
4.9%
s 6026
 
4.9%
Other values (9) 23534
19.3%
Common
ValueCountFrequency (%)
6026
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 127949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 16020
12.5%
i 14173
11.1%
p 12052
9.4%
f 12052
9.4%
u 10268
 
8.0%
c 7873
 
6.2%
l 7873
 
6.2%
O 6026
 
4.7%
S 6026
 
4.7%
6026
 
4.7%
Other values (10) 29560
23.1%

Sub-Category
Categorical

High correlation 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size548.5 KiB
Binders
1523 
Paper
1370 
Furnishings
957 
Phones
889 
Storage
846 
Other values (12)
4409 

Length

Max length11
Median length9
Mean length7.191715
Min length3

Characters and Unicode

Total characters71874
Distinct characters28
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBookcases
2nd rowChairs
3rd rowLabels
4th rowTables
5th rowStorage

Common Values

ValueCountFrequency (%)
Binders 1523
15.2%
Paper 1370
13.7%
Furnishings 957
9.6%
Phones 889
8.9%
Storage 846
8.5%
Art 796
8.0%
Accessories 775
7.8%
Chairs 617
6.2%
Appliances 466
 
4.7%
Labels 364
 
3.6%
Other values (7) 1391
13.9%

Length

2025-02-20T21:42:22.901660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
binders 1523
15.2%
paper 1370
13.7%
furnishings 957
9.6%
phones 889
8.9%
storage 846
8.5%
art 796
8.0%
accessories 775
7.8%
chairs 617
6.2%
appliances 466
 
4.7%
labels 364
 
3.6%
Other values (7) 1391
13.9%

Most occurring characters

ValueCountFrequency (%)
s 9934
13.8%
e 8870
12.3%
r 7169
 
10.0%
i 5668
 
7.9%
n 5378
 
7.5%
a 4542
 
6.3%
o 3288
 
4.6%
p 3004
 
4.2%
h 2578
 
3.6%
c 2359
 
3.3%
Other values (18) 19084
26.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 61880
86.1%
Uppercase Letter 9994
 
13.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 9934
16.1%
e 8870
14.3%
r 7169
11.6%
i 5668
9.2%
n 5378
8.7%
a 4542
7.3%
o 3288
 
5.3%
p 3004
 
4.9%
h 2578
 
4.2%
c 2359
 
3.8%
Other values (8) 9090
14.7%
Uppercase Letter
ValueCountFrequency (%)
P 2259
22.6%
A 2037
20.4%
B 1751
17.5%
F 1174
11.7%
S 1036
10.4%
C 685
 
6.9%
L 364
 
3.6%
T 319
 
3.2%
E 254
 
2.5%
M 115
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 71874
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 9934
13.8%
e 8870
12.3%
r 7169
 
10.0%
i 5668
 
7.9%
n 5378
 
7.5%
a 4542
 
6.3%
o 3288
 
4.6%
p 3004
 
4.2%
h 2578
 
3.6%
c 2359
 
3.3%
Other values (18) 19084
26.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71874
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 9934
13.8%
e 8870
12.3%
r 7169
 
10.0%
i 5668
 
7.9%
n 5378
 
7.5%
a 4542
 
6.3%
o 3288
 
4.6%
p 3004
 
4.2%
h 2578
 
3.6%
c 2359
 
3.3%
Other values (18) 19084
26.6%
Distinct1850
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Memory size859.3 KiB
2025-02-20T21:42:23.022981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length127
Median length78
Mean length36.914449
Min length5

Characters and Unicode

Total characters368923
Distinct characters85
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)0.9%

Sample

1st rowBush Somerset Collection Bookcase
2nd rowHon Deluxe Fabric Upholstered Stacking Chairs, Rounded Back
3rd rowSelf-Adhesive Address Labels for Typewriters by Universal
4th rowBretford CR4500 Series Slim Rectangular Table
5th rowEldon Fold 'N Roll Cart System
ValueCountFrequency (%)
xerox 865
 
1.5%
x 701
 
1.3%
599
 
1.1%
with 599
 
1.1%
avery 557
 
1.0%
for 539
 
1.0%
binders 524
 
0.9%
chair 479
 
0.9%
black 426
 
0.8%
phone 374
 
0.7%
Other values (2798) 50371
89.9%
2025-02-20T21:42:23.206698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
45654
 
12.4%
e 33538
 
9.1%
r 20791
 
5.6%
o 19902
 
5.4%
a 19064
 
5.2%
i 18648
 
5.1%
l 16365
 
4.4%
n 15622
 
4.2%
s 14683
 
4.0%
t 14550
 
3.9%
Other values (75) 150106
40.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 238253
64.6%
Uppercase Letter 56270
 
15.3%
Space Separator 46081
 
12.5%
Decimal Number 17981
 
4.9%
Other Punctuation 7152
 
1.9%
Dash Punctuation 2940
 
0.8%
Final Punctuation 67
 
< 0.1%
Close Punctuation 60
 
< 0.1%
Open Punctuation 60
 
< 0.1%
Math Symbol 35
 
< 0.1%
Other values (2) 24
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 33538
14.1%
r 20791
 
8.7%
o 19902
 
8.4%
a 19064
 
8.0%
i 18648
 
7.8%
l 16365
 
6.9%
n 15622
 
6.6%
s 14683
 
6.2%
t 14550
 
6.1%
c 8924
 
3.7%
Other values (18) 56166
23.6%
Uppercase Letter
ValueCountFrequency (%)
S 6281
 
11.2%
C 6007
 
10.7%
B 5530
 
9.8%
P 4918
 
8.7%
A 2948
 
5.2%
D 2941
 
5.2%
M 2870
 
5.1%
T 2616
 
4.6%
F 2510
 
4.5%
L 2284
 
4.1%
Other values (16) 17365
30.9%
Other Punctuation
ValueCountFrequency (%)
, 3120
43.6%
/ 1561
21.8%
" 1300
18.2%
. 463
 
6.5%
& 287
 
4.0%
' 257
 
3.6%
# 90
 
1.3%
% 45
 
0.6%
! 9
 
0.1%
* 9
 
0.1%
Other values (2) 11
 
0.2%
Decimal Number
ValueCountFrequency (%)
1 3783
21.0%
0 2921
16.2%
2 2270
12.6%
4 1725
9.6%
3 1530
8.5%
5 1443
 
8.0%
8 1254
 
7.0%
9 1234
 
6.9%
6 941
 
5.2%
7 880
 
4.9%
Space Separator
ValueCountFrequency (%)
45654
99.1%
  427
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
- 2940
100.0%
Final Punctuation
ValueCountFrequency (%)
67
100.0%
Close Punctuation
ValueCountFrequency (%)
) 60
100.0%
Open Punctuation
ValueCountFrequency (%)
( 60
100.0%
Math Symbol
ValueCountFrequency (%)
+ 35
100.0%
Initial Punctuation
ValueCountFrequency (%)
19
100.0%
Other Number
ValueCountFrequency (%)
¾ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 294523
79.8%
Common 74400
 
20.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 33538
 
11.4%
r 20791
 
7.1%
o 19902
 
6.8%
a 19064
 
6.5%
i 18648
 
6.3%
l 16365
 
5.6%
n 15622
 
5.3%
s 14683
 
5.0%
t 14550
 
4.9%
c 8924
 
3.0%
Other values (44) 112436
38.2%
Common
ValueCountFrequency (%)
45654
61.4%
1 3783
 
5.1%
, 3120
 
4.2%
- 2940
 
4.0%
0 2921
 
3.9%
2 2270
 
3.1%
4 1725
 
2.3%
/ 1561
 
2.1%
3 1530
 
2.1%
5 1443
 
1.9%
Other values (21) 7453
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 368388
99.9%
None 449
 
0.1%
Punctuation 86
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
45654
 
12.4%
e 33538
 
9.1%
r 20791
 
5.6%
o 19902
 
5.4%
a 19064
 
5.2%
i 18648
 
5.1%
l 16365
 
4.4%
n 15622
 
4.2%
s 14683
 
4.0%
t 14550
 
3.9%
Other values (69) 149571
40.6%
None
ValueCountFrequency (%)
  427
95.1%
é 14
 
3.1%
¾ 5
 
1.1%
à 3
 
0.7%
Punctuation
ValueCountFrequency (%)
67
77.9%
19
 
22.1%

Returned
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size507.6 KiB
Not
9194 
Yes
 
800

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters29982
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot
2nd rowNot
3rd rowNot
4th rowNot
5th rowNot

Common Values

ValueCountFrequency (%)
Not 9194
92.0%
Yes 800
 
8.0%

Length

2025-02-20T21:42:23.251393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T21:42:23.278853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
not 9194
92.0%
yes 800
 
8.0%

Most occurring characters

ValueCountFrequency (%)
N 9194
30.7%
o 9194
30.7%
t 9194
30.7%
Y 800
 
2.7%
e 800
 
2.7%
s 800
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19988
66.7%
Uppercase Letter 9994
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 9194
46.0%
t 9194
46.0%
e 800
 
4.0%
s 800
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
N 9194
92.0%
Y 800
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 29982
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 9194
30.7%
o 9194
30.7%
t 9194
30.7%
Y 800
 
2.7%
e 800
 
2.7%
s 800
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29982
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 9194
30.7%
o 9194
30.7%
t 9194
30.7%
Y 800
 
2.7%
e 800
 
2.7%
s 800
 
2.7%

Sales
Real number (ℝ)

High correlation 

Distinct5825
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean229.858
Minimum0.444
Maximum22638.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-02-20T21:42:23.320650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.444
5-th percentile4.98
Q117.28
median54.49
Q3209.94
95-th percentile956.98425
Maximum22638.48
Range22638.036
Interquartile range (IQR)192.66

Descriptive statistics

Standard deviation623.2451
Coefficient of variation (CV)2.7114353
Kurtosis305.31175
Mean229.858
Median Absolute Deviation (MAD)45.406
Skewness12.972752
Sum2297200.9
Variance388434.46
MonotonicityNot monotonic
2025-02-20T21:42:23.375619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.96 56
 
0.6%
19.44 39
 
0.4%
15.552 39
 
0.4%
10.368 36
 
0.4%
25.92 36
 
0.4%
32.4 28
 
0.3%
6.48 21
 
0.2%
17.94 21
 
0.2%
20.736 19
 
0.2%
14.94 17
 
0.2%
Other values (5815) 9682
96.9%
ValueCountFrequency (%)
0.444 1
 
< 0.1%
0.556 1
 
< 0.1%
0.836 1
 
< 0.1%
0.852 1
 
< 0.1%
0.876 1
 
< 0.1%
0.898 1
 
< 0.1%
0.984 1
 
< 0.1%
0.99 1
 
< 0.1%
1.044 1
 
< 0.1%
1.08 3
< 0.1%
ValueCountFrequency (%)
22638.48 1
< 0.1%
17499.95 1
< 0.1%
13999.96 1
< 0.1%
11199.968 1
< 0.1%
10499.97 1
< 0.1%
9892.74 1
< 0.1%
9449.95 1
< 0.1%
9099.93 1
< 0.1%
8749.95 1
< 0.1%
8399.976 1
< 0.1%

Quantity
Real number (ℝ)

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7895737
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-02-20T21:42:23.416297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum14
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2251097
Coefficient of variation (CV)0.58716622
Kurtosis1.9918894
Mean3.7895737
Median Absolute Deviation (MAD)1
Skewness1.2785448
Sum37873
Variance4.9511131
MonotonicityNot monotonic
2025-02-20T21:42:23.461126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 2409
24.1%
2 2402
24.0%
5 1230
12.3%
4 1191
11.9%
1 899
 
9.0%
7 606
 
6.1%
6 572
 
5.7%
9 258
 
2.6%
8 257
 
2.6%
10 57
 
0.6%
Other values (4) 113
 
1.1%
ValueCountFrequency (%)
1 899
 
9.0%
2 2402
24.0%
3 2409
24.1%
4 1191
11.9%
5 1230
12.3%
6 572
 
5.7%
7 606
 
6.1%
8 257
 
2.6%
9 258
 
2.6%
10 57
 
0.6%
ValueCountFrequency (%)
14 29
 
0.3%
13 27
 
0.3%
12 23
 
0.2%
11 34
 
0.3%
10 57
 
0.6%
9 258
 
2.6%
8 257
 
2.6%
7 606
6.1%
6 572
5.7%
5 1230
12.3%

Discount
Real number (ℝ)

High correlation  Zeros 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15620272
Minimum0
Maximum0.8
Zeros4798
Zeros (%)48.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-02-20T21:42:23.503005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.2
Q30.2
95-th percentile0.7
Maximum0.8
Range0.8
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.20645197
Coefficient of variation (CV)1.3216925
Kurtosis2.4095461
Mean0.15620272
Median Absolute Deviation (MAD)0.2
Skewness1.6842947
Sum1561.09
Variance0.042622415
MonotonicityNot monotonic
2025-02-20T21:42:23.551075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 4798
48.0%
0.2 3657
36.6%
0.7 418
 
4.2%
0.8 300
 
3.0%
0.3 227
 
2.3%
0.4 206
 
2.1%
0.6 138
 
1.4%
0.1 94
 
0.9%
0.5 66
 
0.7%
0.15 52
 
0.5%
Other values (2) 38
 
0.4%
ValueCountFrequency (%)
0 4798
48.0%
0.1 94
 
0.9%
0.15 52
 
0.5%
0.2 3657
36.6%
0.3 227
 
2.3%
0.32 27
 
0.3%
0.4 206
 
2.1%
0.45 11
 
0.1%
0.5 66
 
0.7%
0.6 138
 
1.4%
ValueCountFrequency (%)
0.8 300
 
3.0%
0.7 418
 
4.2%
0.6 138
 
1.4%
0.5 66
 
0.7%
0.45 11
 
0.1%
0.4 206
 
2.1%
0.32 27
 
0.3%
0.3 227
 
2.3%
0.2 3657
36.6%
0.15 52
 
0.5%

Profit
Real number (ℝ)

High correlation 

Distinct7287
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.656896
Minimum-6599.978
Maximum8399.976
Zeros65
Zeros (%)0.7%
Negative1871
Negative (%)18.7%
Memory size78.2 KiB
2025-02-20T21:42:23.603855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-6599.978
5-th percentile-53.03092
Q11.72875
median8.6665
Q329.364
95-th percentile168.4704
Maximum8399.976
Range14999.954
Interquartile range (IQR)27.63525

Descriptive statistics

Standard deviation234.26011
Coefficient of variation (CV)8.1746504
Kurtosis397.18851
Mean28.656896
Median Absolute Deviation (MAD)10.77855
Skewness7.5614316
Sum286397.02
Variance54877.798
MonotonicityNot monotonic
2025-02-20T21:42:23.659763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 65
 
0.7%
6.2208 43
 
0.4%
9.3312 38
 
0.4%
5.4432 32
 
0.3%
3.6288 32
 
0.3%
15.552 26
 
0.3%
12.4416 21
 
0.2%
7.2576 19
 
0.2%
3.1104 18
 
0.2%
9.072 11
 
0.1%
Other values (7277) 9689
96.9%
ValueCountFrequency (%)
-6599.978 1
< 0.1%
-3839.9904 1
< 0.1%
-3701.8928 1
< 0.1%
-3399.98 1
< 0.1%
-2929.4845 1
< 0.1%
-2639.9912 1
< 0.1%
-2287.782 1
< 0.1%
-1862.3124 1
< 0.1%
-1850.9464 1
< 0.1%
-1811.0784 1
< 0.1%
ValueCountFrequency (%)
8399.976 1
< 0.1%
6719.9808 1
< 0.1%
5039.9856 1
< 0.1%
4946.37 1
< 0.1%
4630.4755 1
< 0.1%
3919.9888 1
< 0.1%
3177.475 1
< 0.1%
2799.984 1
< 0.1%
2591.9568 1
< 0.1%
2504.2216 1
< 0.1%

Interactions

2025-02-20T21:42:19.335665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:17.900969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:18.196077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:18.481242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:18.749681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:19.026600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:19.387530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:17.950784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:18.246672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:18.525704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:18.794709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:19.075660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:19.441045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:17.996999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:18.291233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:18.570572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:18.841988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:19.126473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:19.489639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:18.046138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:18.336798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:18.611282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:18.885728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:19.174452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:19.541485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:18.094746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:18.381653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:18.654407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:18.931415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:19.224717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:20.066417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:18.146168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:18.429992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:18.701372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:18.981271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T21:42:19.279099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-20T21:42:23.706188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
CategoryDiscountPostal CodeProfitQuantityRegionRetail Sales PeopleReturnedRow IDSalesSegmentShip ModeStateSub-Category
Category1.0000.3770.0000.0560.0000.0000.0000.0000.0080.0720.0000.0000.0190.999
Discount0.3771.0000.053-0.543-0.0010.2940.2940.0290.013-0.0570.0050.0270.3540.353
Postal Code0.0000.0531.000-0.0050.0140.9210.9210.1880.011-0.0020.0350.0380.9680.000
Profit0.056-0.543-0.0051.0000.2340.0210.0210.031-0.0110.5180.0000.0050.0170.130
Quantity0.000-0.0010.0140.2341.0000.0000.0000.000-0.0020.3270.0120.0000.0040.000
Region0.0000.2940.9210.0210.0001.0001.0000.1850.0380.0000.0000.0220.9980.000
Retail Sales People0.0000.2940.9210.0210.0001.0001.0000.1850.0380.0000.0000.0220.9980.000
Returned0.0000.0290.1880.0310.0000.1850.1851.0000.0310.0260.0210.0450.1950.020
Row ID0.0080.0130.011-0.011-0.0020.0380.0380.0311.000-0.0010.0300.0500.1020.000
Sales0.072-0.057-0.0020.5180.3270.0000.0000.026-0.0011.0000.0020.0000.0000.142
Segment0.0000.0050.0350.0000.0120.0000.0000.0210.0300.0021.0000.0330.0900.000
Ship Mode0.0000.0270.0380.0050.0000.0220.0220.0450.0500.0000.0331.0000.0960.007
State0.0190.3540.9680.0170.0040.9980.9980.1950.1020.0000.0900.0961.0000.000
Sub-Category0.9990.3530.0000.1300.0000.0000.0000.0200.0000.1420.0000.0070.0001.000

Missing values

2025-02-20T21:42:20.177445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-20T21:42:20.279550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Row IDOrder IDOrder DateShip DateShip ModeCustomer IDCustomer NameSegmentCountryCityStatePostal CodeRegionRetail Sales PeopleProduct IDCategorySub-CategoryProduct NameReturnedSalesQuantityDiscountProfit
01CA-2016-1521562016-08-112016-11-11Second ClassCG-12520Claire GuteConsumerUnited StatesHendersonKentucky42420SouthCassandra BrandowFUR-BO-10001798FurnitureBookcasesBush Somerset Collection BookcaseNot261.960020.0041.9136
12CA-2016-1521562016-08-112016-11-11Second ClassCG-12520Claire GuteConsumerUnited StatesHendersonKentucky42420SouthCassandra BrandowFUR-CH-10000454FurnitureChairsHon Deluxe Fabric Upholstered Stacking Chairs, Rounded BackNot731.940030.00219.5820
23CA-2016-1386882016-12-062016-12-06Second ClassDV-13045Darrin Van HuffCorporateUnited StatesLos AngelesCalifornia90036WestAnna AndreadiOFF-LA-10000240Office SuppliesLabelsSelf-Adhesive Address Labels for Typewriters by UniversalNot14.620020.006.8714
34US-2015-1089662015-11-102015-11-10Standard ClassSO-20335Sean O'DonnellConsumerUnited StatesFort LauderdaleFlorida33311SouthCassandra BrandowFUR-TA-10000577FurnitureTablesBretford CR4500 Series Slim Rectangular TableNot957.577550.45-383.0310
45US-2015-1089662015-11-102015-11-10Standard ClassSO-20335Sean O'DonnellConsumerUnited StatesFort LauderdaleFlorida33311SouthCassandra BrandowOFF-ST-10000760Office SuppliesStorageEldon Fold 'N Roll Cart SystemNot22.368020.202.5164
56CA-2014-1158122014-09-062014-09-06Standard ClassBH-11710Brosina HoffmanConsumerUnited StatesLos AngelesCalifornia90032WestAnna AndreadiFUR-FU-10001487FurnitureFurnishingsEldon Expressions Wood and Plastic Desk Accessories, Cherry WoodNot48.860070.0014.1694
67CA-2014-1158122014-09-062014-09-06Standard ClassBH-11710Brosina HoffmanConsumerUnited StatesLos AngelesCalifornia90032WestAnna AndreadiOFF-AR-10002833Office SuppliesArtNewell 322Not7.280040.001.9656
78CA-2014-1158122014-09-062014-09-06Standard ClassBH-11710Brosina HoffmanConsumerUnited StatesLos AngelesCalifornia90032WestAnna AndreadiTEC-PH-10002275TechnologyPhonesMitel 5320 IP Phone VoIP phoneNot907.152060.2090.7152
89CA-2014-1158122014-09-062014-09-06Standard ClassBH-11710Brosina HoffmanConsumerUnited StatesLos AngelesCalifornia90032WestAnna AndreadiOFF-BI-10003910Office SuppliesBindersDXL Angle-View Binders with Locking Rings by SamsillNot18.504030.205.7825
910CA-2014-1158122014-09-062014-09-06Standard ClassBH-11710Brosina HoffmanConsumerUnited StatesLos AngelesCalifornia90032WestAnna AndreadiOFF-AP-10002892Office SuppliesAppliancesBelkin F5C206VTEL 6 Outlet SurgeNot114.900050.0034.4700
Row IDOrder IDOrder DateShip DateShip ModeCustomer IDCustomer NameSegmentCountryCityStatePostal CodeRegionRetail Sales PeopleProduct IDCategorySub-CategoryProduct NameReturnedSalesQuantityDiscountProfit
99849985CA-2015-1002512015-05-172015-05-23Standard ClassDV-13465Dianna VittoriniConsumerUnited StatesLong BeachNew York11561EastChuck MageeOFF-LA-10003766Office SuppliesLabelsSelf-Adhesive Removable LabelsNot31.500100.015.1200
99859986CA-2015-1002512015-05-172015-05-23Standard ClassDV-13465Dianna VittoriniConsumerUnited StatesLong BeachNew York11561EastChuck MageeOFF-SU-10000898Office SuppliesSuppliesAcme Hot Forged Carbon Steel Scissors with Nickel-Plated Handles, 3 7/8" Cut, 8"LNot55.60040.016.1240
99869987CA-2016-1257942016-09-292016-09-29Standard ClassML-17410Maris LaWareConsumerUnited StatesLos AngelesCalifornia90008WestAnna AndreadiTEC-AC-10003399TechnologyAccessoriesMemorex Mini Travel Drive 64 GB USB 2.0 Flash DriveNot36.24010.015.2208
99879988CA-2017-1636292017-11-172017-11-21Standard ClassRA-19885Ruben AusmanCorporateUnited StatesAthensGeorgia30605SouthCassandra BrandowTEC-AC-10001539TechnologyAccessoriesLogitech G430 Surround Sound Gaming Headset with Dolby 7.1 TechnologyNot79.99010.028.7964
99889989CA-2017-1636292017-11-172017-11-21Standard ClassRA-19885Ruben AusmanCorporateUnited StatesAthensGeorgia30605SouthCassandra BrandowTEC-PH-10004006TechnologyPhonesPanasonic KX - TS880B TelephoneNot206.10050.055.6470
99899990CA-2014-1104222014-01-212014-01-23Second ClassTB-21400Tom BoeckenhauerConsumerUnited StatesMiamiFlorida33180SouthCassandra BrandowFUR-FU-10001889FurnitureFurnishingsUltra Door Pull HandleNot25.24830.24.1028
99909991CA-2017-1212582017-02-262017-03-03Standard ClassDB-13060Dave BrooksConsumerUnited StatesCosta MesaCalifornia92627WestAnna AndreadiFUR-FU-10000747FurnitureFurnishingsTenex B1-RE Series Chair Mats for Low Pile CarpetsYes91.96020.015.6332
99919992CA-2017-1212582017-02-262017-03-03Standard ClassDB-13060Dave BrooksConsumerUnited StatesCosta MesaCalifornia92627WestAnna AndreadiTEC-PH-10003645TechnologyPhonesAastra 57i VoIP phoneYes258.57620.219.3932
99929993CA-2017-1212582017-02-262017-03-03Standard ClassDB-13060Dave BrooksConsumerUnited StatesCosta MesaCalifornia92627WestAnna AndreadiOFF-PA-10004041Office SuppliesPaperIt's Hot Message Books with Stickers, 2 3/4" x 5"Yes29.60040.013.3200
99939994CA-2017-1199142017-04-052017-09-05Second ClassCC-12220Chris CortesConsumerUnited StatesWestminsterCalifornia92683WestAnna AndreadiOFF-AP-10002684Office SuppliesAppliancesAcco 7-Outlet Masterpiece Power Center, Wihtout Fax/Phone Line ProtectionNot243.16020.072.9480